Framework for monitoring, controlling, reporting, recording and data authentication of grain storage container systems

A system, comprising:

• a data collection element configured to receive input data comprised of information collected from a plurality of sensors that are associated a) with auxiliary devices in one or more bin systems within or near a grain container, b) with one or more sections of a grain mass stored in the grain storage container, and c) with the grain storage container;
• and
• a system health model configured, within a computing environment that includes one or more processors and at least one computer-readable non-transitory storage medium having program instructions stored therein which, when executed by the one or more processors, cause the one or more processors to execute a plurality of data processing elements to monitor and control the bin systems, by:
• estimating at least one manipulated variable representing a condition within the grain mass over time, in a set of machine learning-based control models that include a testing model that is trained on historical data that includes additional variables representing characteristics of the grain mass, and validated by newly-collected input data that includes the additional variables, the testing model generating an estimated output at a specified time step representing a future time, and a production model configured to continuously adjust the testing model by measuring the estimated output at a time step representing a future time against a realized output as the time step occurs,
• generating a system health profile representing both grain mass conditions and operational characteristics of the bin systems from the estimated output of the testing model, and
• predicting the at least one manipulated variable for the grain mass from the system health profile;
• and
• wherein an operation of the auxiliary devices in the one or more bin systems is controlled based on the system health profile.

A method, comprising:

• estimating at least one manipulated variable representing a condition within a grain mass over time to monitor and control an operation of one or more bin systems comprised of auxiliary devices and within or near a grain storage container, in a set of machine learning-based control models that analyze input data comprised of information collected from a plurality of sensors that are associated a) with the auxiliary devices in the one or more bin systems within the grain container, b) with one or more sections of a grain mass stored in the grain storage container, and c) with the grain storage container, the set of machine learning-based control models including a testing model that is trained on historical data that includes additional variables representing characteristics of the grain mass, and validated by newly-collected input data that includes the additional variables, the testing model generating an estimated output at a specified time step representing a future time, and a production model configured to continuously adjust the testing model by measuring the estimated output at a time step representing a future time against a realized output as the time step occurs;
• generating a system health profile representing both grain mass conditions and operational characteristics of the bin systems from the estimated output of the testing model;
• and
• predicting the at least one manipulated variable for the grain mass from the system health profile,
• wherein the operation of the auxiliary devices in the one or more bin systems is controlled based on the system health profile.